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Integration: iGraph

Genes are sequences of DNA that encode functional molecules (such as RNAs or Proteins). These functional molecules most of time interact and affect eachothers function. Therefor, to understand the role of a specific gene in a disease, scientist need to know about all the published functions.

Given many published scientific manuscripts about any given gene, the National Center for Biotechnology Information (NCBI) has introduced GeneRIF. GeneRIF provides scientist a simple mechanism to add functional annotation to known genes based on scientific publications. These annotations are than processed and approved by GeneRIF staff. All these functional annotations are freely accessible and can be used by anyone to extract functions of a gene or genes with specific function.

This workflow imports human Phophatases and Kinases and the publications. And uses ‘igraph’ to store this information and to plot the relation between these publications as well as interaction network between molecules.

digraph INTEGRATE_liver { compound=true; print [label = "print()"]; data_pk [shape = invhouse, label = "human PPases & \nkinases from GO"]; data_sql [shape = invhouse, label = "SQL query \nget_all_RIF.sql"]; data_rif [shape = invhouse, label = "RIFs \nfrom entrez"]; read [shape = box, label = "Read\ngunzip()\nread.delim()"]; edges [shape = box, label = "Extract edges\nsqldf()"]; network [shape = box; label = "Make graph object\ngraph.data.frame() "]; subset [label = "subset"]; network2 [shape = box; label = "Make graph object\ngraph.data.frame() "]; decomp [shape = box; label = "Decompose\ndecompose.graph() "]; largest [label = "select largest \ncomponent"] vertices [shape = box; label = "extract vertices \nget.data.frame() "]; cocitation [shape = box; label = "peform cocitation\ngraph.adjacency(cocitation()) "]; subgrph [shape = box; label = "subset gene nodes\ninduced.subgraph()"]; nodeprop [label = "change node \nproperties"]; getdata [label = "get.data.frame()"]; subgraph cluster_0 { label = "Load data"; style = filled; color = lightgrey; data_rif -> read; read -> query; query -> edges; edges -> network; data_sql -> edges; } subgraph cluster_1 { label = "Decompose"; style = filled; color = lightgrey; node [style = filled, color = white]; decomp -> largest; } subgraph cluster_2 { label = "Do cocitation"; style = filled; color = lightgrey; node [style = filled, color = white]; vertices -> cocitation; cocitation -> subgrph; } network -> subset [ltail = cluster_0]; data_pk -> subset -> network2; network2 -> decomp [lhead = cluster_1]; largest -> vertices [ltail = cluster_1, lhead = cluster_2]; subgrph-> nodeprop [ltail = cluster_2]; nodeprop -> getdata -> print; { rank=same; network -> subgrph [style=invis]; } # do.load: data_rif->read->query->edges->network;data_sql->edges; # do.mesh terms [shape = invhouse; label = "Ontology terms"]; pubmed [shape = box; label = "Query PubMed"]; listxml [shape = box; label = "xmlToList(xmlParse())"]; network3 [shape = box; label = "Make graph object\ngraph.data.frame() "]; filter [label = "Filter"]; terms -> pubmed -> listxml -> filter -> network3; edges -> filter [ltail = cluster_0]; network3 -> decomp [lhead = cluster_1]; }

Workflow for integration liver cohort data.

Packages and Dependencies

There are 7 packages used in this workflow, which depend on 35 additional packages from CRAN (dependencies)

Used packages:

  • CRAN: igraph, XML, R.utils, plyr, reshape, utils, sqldf

Package dependencies:

  • CRAN: magrittr, irlba, Matrix, NMF, lattice, foreach, gridBase, pkgmaker, reshape2, stringr, colorspace, doParallel, digest, rngtools, ggplot2, RColorBrewer, cluster, registry, codetools, iterators, xtable, plyr, Rcpp, stringi, scales, gtable, MASS, proto, dichromat, labeling, munsell, RSQLite, gsubfn, chron, DBI

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